Early Survival Prediction Framework in CD19-Specific CAR-T Cell Immunotherapy Using a Quantitative Systems Pharmacology Model

Abstract

Chimeric antigen receptor (CAR)-T cell therapy has revolutionized treatment of relapsed/refractory non-Hodgkin lymphoma (NHL). However, since 36–60% of patients relapse, early response prediction is crucial. We present a novel population quantitative systems pharmacology model, integrating literature knowledge on physiology, immunology, and adoptive cell therapy together with 133 CAR-T cell phenotype, 1943 cytokine, and 48 metabolic tumor measurements. The model well described post-infusion concentrations of four CAR-T cell phenotypes and CD19+ metabolic tumor volume over 3 months after CAR-T cell infusion. Leveraging the model, we identified a low expansion subpopulation with significantly lower CAR-T cell expansion capacities amongst 19 NHL patients. Together with two patient-/therapy-related factors (autologous stem cell transplantation, CD4+/CD8+ T cells), the low expansion subpopulation explained 2/3 of the interindividual variability in the CAR-T cell expansion capacities. Moreover, the low expansion subpopulation had poor prognosis as only 1/4 of the low expansion subpopulation compared to 2/3 of the reference population were still alive after 24 months. We translated the expansion capacities into a clinical composite score (CCS) of ‘Maximum naïve CAR-T cell concentrations/Baseline tumor burden’ ratio and propose a CCSTN-value > 0.00136 (cellsµL−1mL−1 as predictor for survival. Once validated in a larger cohort, the model will foster refining survival prediction and solutions to enhance NHL CAR-T cell therapy response.

Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapse

“…Prediction of the late response of patients to CAR T-cell therapy during early treatment stage will greatly improve patient outcomes by guiding the treatment regimen that follows, especially for patients with acute disease progression. [36][37][38] Having demonstrated that our computational model accurately recapitulated the cellular dynamics of CAR T-cell therapy, we found that the response can be differentiated by the actual function level and dynamics of CAR T-cells in individual patients. Thus, we hypothesized Open access that such responses can be predicted using our computational immuno-oncology model with input of clinically measurable and available patient information related to CAR T-cell dynamics, such as the peak value and AUC7 (area under the curve from days 0 to 7) of CAR T-cells, at the early stage of CAR T-cell treatment.…”

Development of minimal physiologically-based pharmacokinetic-pharmacodynamic models for characterizing cellular kinetics of CAR T cells following local deliveries in mice

“…administration, whereas lower volumetric blood flow rate at the hepatic artery injection site near the tumor is beneficial for CAR T distribution to the hepatic tumor. As current investigation brought insights into the contribution of blood flow to CAR T distribution to solid tumors, future effort will strive to understand migration behavior of heterogenous CAR T subsets and phenotypes and the impact on its safety and efficacy [ 32 ].…”

Clinical Pharmacology Perspectives for Adoptive Cell Therapies in Oncology

“…The challenge of this approach is the requirement of enormous T cell data measured by flow cytometry for each kind/ species, as well as longitudinal data of tumor dynamics to capture the antigen-dependent T cell kinetics. A recent model by Mueller-Schoell et al 35 incorporated four phenotypes, including naïve, central memory, effector memory, and terminally differentiated effector T cells and enabled both T cell-mediated tumor killing as well as tumor-dependent T cell proliferation. 35 However, due to the sparse data from only 19 patients, several parameters, including tumor growth rate, homeostatic proliferation rate constants, and death rate constants, are unidentifiable, and only two parameters were allowed to consider IIV during fitting.…”

“…The challenge of this approach is the requirement of enormous T cell data measured by flow cytometry for each kind/species, as well as longitudinal data of tumor dynamics to capture the antigen‐dependent T cell kinetics. A recent model by Mueller‐Schoell et al 35 . incorporated four phenotypes, including naïve, central memory, effector memory, and terminally differentiated effector T cells and enabled both T cell‐mediated tumor killing as well as tumor‐dependent T cell proliferation 35 .…”